Update README.md

README.md

@@ -10,25 +10,25 @@ base_model:
 
 
 
-# CALM-8B: Conversational Agentic Language Model
+# CoALM-8B: Conversational Agentic Language Model
 
 [![Made with Oumi](https://badgen.net/badge/Made%20with/Oumi/%23085CFF?icon=https%3A%2F%2Foumi.ai%2Flogo_dark.svg)](https://github.com/oumi-ai/oumi)
 
 
 
 ## Model Description
-**CALM-8B** is the smallest open-source model of **CALM** (Conversational Agentic Language Model) series, designed to integrate both **Task-Oriented Dialogue (TOD) capabilities** and **Language Agent (LA) functionalities** into a unified system. By fine-tuning on **CALM-IT**, a novel dataset that interleaves multi-turn ReAct-based reasoning with complex API usage, CALM-8B achieves promising results on TOD and function-calling benchmarks.
+**CoALM-8B** is the smallest open-source model of **CoALM** (Conversational Agentic Language Model) series, designed to integrate both **Task-Oriented Dialogue (TOD) capabilities** and **Language Agent (LA) functionalities** into a unified system. By fine-tuning on **CoALM-IT**, a novel dataset that interleaves multi-turn ReAct-based reasoning with complex API usage, CoALM-8B achieves promising results on TOD and function-calling benchmarks.
 
-CALM-8B is trained on a **multi-task dataset** covering dialogue state tracking, function calling, and multi-turn reasoning. The model outperforms top domain-specific models on key evaluation benchmarks: **MultiWOZ 2.4 (TOD), BFCL V3 (LA), and API-Bank (LA).**
+CoALM-8B is trained on a **multi-task dataset** covering dialogue state tracking, function calling, and multi-turn reasoning. The model outperforms top domain-specific models on key evaluation benchmarks: **MultiWOZ 2.4 (TOD), BFCL V3 (LA), and API-Bank (LA).**
 
 ## Model Sources
 
 <!-- Provide the basic links for the model. -->
 
 - 📝 **Paper:** https://arxiv.org/abs/2502.08820
-- 🌐 **Project Page:** https://emrecanacikgoz.github.io/CALM/
+- 🌐 **Project Page:** https://emrecanacikgoz.github.io/CoALM/
 - 💻 **Repository:** https://github.com/oumi-ai/oumi/tree/main/configs/projects/calm
-- 💎 **Dataset:** https://huggingface.co/datasets/uiuc-convai/CALM-IT
+- 💎 **Dataset:** https://huggingface.co/datasets/uiuc-convai/CoALM-IT
 
 
 
@@ -36,11 +36,11 @@ CALM-8B is trained on a **multi-task dataset** covering dialogue state tracking,
 ---
 ## Model Details
 
-- **Model Name:** CALM-8B  
+- **Model Name:** CoALM-8B  
 - **Developed by:** Colloboration of UIUC Conversational AI LAB and Oumi 
 - **License:** cc-by-nc-4.0  
 - **Architecture:** Fine-tuned **Llama 3.1 8B Instruct**  
-- **Training Data:** CALM-IT dataset
+- **Training Data:** CoALM-IT dataset
 - **Fine-tuning Framework:** [Oumi](https://github.com/oumi-ai/oumi)
 - **Training Hardware:** 8 NVIDIA H100 GPUs  
 - **Training Duration:** ~8 hours  
@@ -79,7 +79,7 @@ CALM-8B is trained on a **multi-task dataset** covering dialogue state tracking,
 - **Gradient Accumulation Steps:** 1
 
 ---
-## 💡 CALM-IT Dataset
+## 💡 CoALM-IT Dataset
 <img src="table.png" alt="CALM-IT Dataset Statistics" width="800"/>
 
 
@@ -95,8 +95,8 @@ CALM-8B is trained on a **multi-task dataset** covering dialogue state tracking,
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("uiuc-convai/CALM-8B")
-model = AutoModelForCausalLM.from_pretrained("uiuc-convai/CALM-8B")
+tokenizer = AutoTokenizer.from_pretrained("uiuc-convai/CoALM-8B")
+model = AutoModelForCausalLM.from_pretrained("uiuc-convai/CoALM-8B")
 ```
 
 ### 🛠 Example Oumi Inference
@@ -116,8 +116,8 @@ oumi train -c ./oumi_train.yaml
 ```
 
 ---
-- **Task-Specific Calibration:** While CALM-8B generalizes well across tasks, performance can improve with domain-specific fine-tuning.
-- **Scalability to Larger Models:** Future iterations (CALM-70B, CALM-405B) extend capabilities to larger-scale agentic conversations.
+- **Task-Specific Calibration:** While CoALM-8B generalizes well across tasks, performance can improve with domain-specific fine-tuning.
+- **Scalability to Larger Models:** Future iterations (CoALM-70B, CoALM-405B) extend capabilities to larger-scale agentic conversations.
 - **Open-Source Expansion:** All datasets, training scripts, and model checkpoints are publicly available to foster further research.
 
 ## Acknowledgements
@@ -132,7 +132,7 @@ This model is licensed under [Creative Commons NonCommercial (CC BY-NC 4.0)](htt
 If you use **CALM-8B** in your research, please cite:
 ```
 @misc{acikgoz2025singlemodelmastermultiturn,
-      title={Can a Single Model Master Both Multi-turn Conversations and Tool Use? CALM: A Unified Conversational Agentic Language Model}, 
+      title={Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model}, 
       author={Emre Can Acikgoz and Jeremiah Greer and Akul Datta and Ze Yang and William Zeng and Oussama Elachqar and Emmanouil Koukoumidis and Dilek Hakkani-Tür and Gokhan Tur},
       year={2025},
       eprint={2502.08820},